• DocumentCode
    6920
  • Title

    At Low SNR, Asymmetric Quantizers are Better

  • Author

    Koch, Thorsten ; Lapidoth, Amos

  • Author_Institution
    Dept. of Eng., Univ. of Cambridge, Cambridge, UK
  • Volume
    59
  • Issue
    9
  • fYear
    2013
  • fDate
    Sept. 2013
  • Firstpage
    5421
  • Lastpage
    5445
  • Abstract
    We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2/π, i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level.
  • Keywords
    Gaussian channels; Rayleigh channels; channel capacity; quantisation (signal); 1-bit quantizer; Rayleigh-fading channel; SNR; asymmetric signaling constellation; asymmetric threshold quantizers; asymptotic power loss; channel capacity; discrete-time Gaussian channel; flash-signaling input distribution; signal-to-noise ratio; spectral efficiency; Ash; Channel capacity; Constellation diagram; Decoding; Quantization (signal); Rayleigh channels; Signal to noise ratio; Capacity per unit energy; Gaussian channel; channel capacity; low signal-to-noise ratio (SNR); quantization;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2013.2262919
  • Filename
    6545291